Neural networks applied to wireless communications

This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Modeling dynamic interactions in supply chains using agentbased simulations

In this work, we present preliminary results of our research on the construction of an agent-based simulation framework suitable to support the analysis of complex supply chain interactions as the one required for the performance assessment in collaborative supply chains. In particular we focus in the modeling of dynamic interactions through agent-to-agent message communication avoiding predefined supply chain network structures. For defining the internal structure of agents we explore the application of the SCOR reference model to bring a business process perspective and adopt the requirement of making explicit separation of the execution and control and decision making processes.Sociedad Argentina de Informática e Investigación Operativ

Ontology Network for Social Network Analysis in a Knowledge Management Context

Organizational knowledge is one of the most valuable assets that companies own today. For several decades organizations have been developing strategies to manage knowledge with particular emphasis on tacit knowledge discovery. The particular dynamic that presents the evolution and transfer of tacit knowledge is closely tied to the relations between people. For this reason, Social Network Analysis (SNA) can be a powerful tool to support a Knowledge Management (KM) initiative. Despite usefulness recognition of SNA techniques within KM processes, there is still remains the initial problem of data collection and representation (problem shared by both initiatives). The aim of this paper is to analyze an ontology network usefulness to obtain the necessary knowledge structure to feed the SNA-KM integration architecture proposed.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Modeling Transport in Service-Oriented Framework for Agent-Based Simulations of Supply Chains

This work presents an extension to the service-oriented framework for performing agent-based simulations to support the analysis of collaborative relationships in supply chain interactions already described in [5]. The extension includes a new service for simulating the planning of transportation, which is designed to allocate finite transportation resources to the transportation orders. The service includes the functionality that matches the required vehicle type, location and time to track the availability of a vehicle fleet as it is allocated to a plan. The new service proposal is validated by simulating three different scenarios of a case study. The case study is based on an actual supply chain of dairy products covering the central region of Argentina.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Data mining use for learning process design of an information source locator agent

The aim of this work is to present a data mining application to software engineering. We describe the use of data mining in some parts of the design process of a dynamic decision support system agent-based architecture. The main function of this system is to guide information requirements from users to the domains that offer greater possibilities of answering them. For that purpose, a strategy is developed, which provides the system with capacity for analyzing an information requirement, and determining to which domains it will be directed. To learn from errors made during its operation, a learning mechanism based in CBR techniques is also proposed. On the one hand, by using data mining techniques it is possible to define a discriminating function to classify the system domains into two groups: those that can probably provide an answer to the information requirement made to the system, and those that cannot. On the other hand, the application of data mining to the cases base allows the specification of rules to settle relationships among the stored cases with the aim of inferring possible causes of error in the domains classification. In this way, a learning mechanism is designed to update the knowledge base and thus improve the already made classification as regards the values assigned to the discriminating function.Eje: Aprendizaje y reconocimiento de patronesRed de Universidades con Carreras en Informática (RedUNCI

Verification of Structured Processes: A Method Based on an Unsoundness Profile

The verification of business processes has been widely studied in the last two decades achieving significant results. Despite this, existing verification techniques based on state space exploration suffer, for large processes, the state space explosion problem. New techniques improved verification performance by structuring processes as trees. However, they do not support complex constructs for advanced synchronization and exception management. To cope with this issue we propose the definition of an unsoundness profile of a given process language, which specifies all possible combinations of control flow constructs that can lead to errors in the behavior of structured processes defined with such a language. In addition, we introduce the sequential and hierarchical soundness properties, which make use of this profile to determine soundness of a structured process with complex constructs in polynomial time. As an example, we defined an unsoundness profile for a subset of the BPMN language and verified the behavior of a BPMN process model.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Modeling dynamic interactions in supply chains using agentbased simulations

In this work, we present preliminary results of our research on the construction of an agent-based simulation framework suitable to support the analysis of complex supply chain interactions as the one required for the performance assessment in collaborative supply chains. In particular we focus in the modeling of dynamic interactions through agent-to-agent message communication avoiding predefined supply chain network structures. For defining the internal structure of agents we explore the application of the SCOR reference model to bring a business process perspective and adopt the requirement of making explicit separation of the execution and control and decision making processes.Sociedad Argentina de Informática e Investigación Operativ

Software Agent Architecture for Managing Inter-Organizational Collaborations

The growing importance of cooperation among organizations, as a result of globalization, current market opportunities and technological advances, encourages organizations to dynamically establish inter-organizational collaborations. These collaborations are carried out by executing collaborative business processes among the organizations. In this work we propose an agent-based software architecture for managing inter-organizational collaborations. Two types of agents are provided: the Collaboration Administrator Agent and the Process Administrator Agent. The former allows organizations setting up collaborations. The latter allows organizations executing collaborative business processes. A Colored Petri Net model specifying the role, which an organization fulfills in a collaborative process, is used to carry out the behavior of the Process Administrator Agent that represents the organization. Planning and execution of the actions of the Process Administrator Agents are driven by a Colored Petri Net machine embedded to them. Thus, Process Administrator Agents do not require to have defined at design-time the protocols they can support. In addition, we propose a model-driven development method for generating Colored Petri Net models from a collaborative process model defined as interaction protocol. Finally, an implementation of the agent-based software architecture and methods based on model-driven development are presented.La creciente importancia de la cooperación entre las organizaciones, como consecuencia de la globalización, las oportunidades actuales de mercado y los avances tecnológicos, alienta a las organizaciones a establecer en forma dinámica colaboraciones inter-organizacionales. Estas colaboraciones se llevan a cabo mediante la ejecución de procesos de negocio colaborativos entre las organizaciones. En este trabajo de investigación se propone una arquitectura basada en agentes de software para la gestión de colaboraciones inter-organizacionales. La arquitectura provee dos tipos de agentes: el Agente Administrador de Colaboraciones y el Agente Administrador de Proceso. El primer agente permite a las organizaciones a establecer colaboraciones. El segundo agente habilita a las organizaciones ejecutar procesos de negocio colaborativos. El rol que una organización desempeña en un proceso colaborativo es especificado mediante un modelo de redes de Petri coloreadas. Este modelo es usado para dirigir el comportamiento del Agente Administrador de Proceso, el cual representa a una organización. La ejecución de los planes y las acciones del Agente Administrador de Proceso son dirigidas mediante una máquina de redes de Petri coloreadas embebida en el agente. Entonces, los Agentes Administrador de Proceso no requieren tener definido en tiempo de diseño los protocolos que dan soporte a su comportamiento. Adicionalmente, se propone un método basado en el desarrollo dirigido por modelos para la generación en forma automática de modelos de redes de Petri coloreadas a partir de un modelo de procesos de negocio colaborativo definido como protocolo de interacción. Finalmente, la implementación de la arquitectura y los métodos basados en el desarrollo dirigido por modelos son presentados.Fil: Tello Leal, Edgar. Universidad Autónoma de Tamaulipas; MéxicoFil: Chiotti, Omar Juan Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaFil: Villarreal, Pablo David. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria En Sistemas de Informacion; Argentin

The importance of context-dependent learning in negotiation agents

Automated negotiation between arti cial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiating agent depends signi cantly on the in uence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is proposed. Also, a simple negotiating agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against negotiating agents in the existing literature, it is shown that it makes no sense to negotiate without taking context-relevant variables into account. The context-aware negotiating agent has been implemented in the GENIUS negotiation environment, and results obtained are signi cant and revealing.Sociedad Argentina de Informática e Investigación Operativ

The importance of context-dependent learning in negotiation agents

Automated negotiation between arti cial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiating agent depends signi cantly on the in uence of environmental conditions or contextual variables, since they affect not only a given agent preferences and strategies, but also those of other agents. Despite this, the existing literature on automated negotiation is scarce about how to properly account for the effect of context-relevant variables in learning and evolving strategies. In this paper, a novel context-driven representation for automated negotiation is proposed. Also, a simple negotiating agent that queries available information from its environment, internally models contextual variables, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes against negotiating agents in the existing literature, it is shown that it makes no sense to negotiate without taking context-relevant variables into account. The context-aware negotiating agent has been implemented in the GENIUS negotiation environment, and results obtained are signi cant and revealing.Sociedad Argentina de Informática e Investigación Operativ